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57 lines
2.0 KiB
57 lines
2.0 KiB
#!/usr/bin/env python
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import numpy as np
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from sklearn.cluster import SpectralClustering as _SpectralClustering
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from sklearn.preprocessing import StandardScaler
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import cexc
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from base import BaseAlgo, ClustererMixin
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from util import df_util
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from util.param_util import convert_params
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class SpectralClustering(ClustererMixin, BaseAlgo):
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def __init__(self, options):
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self.handle_options(options)
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out_params = convert_params(
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options.get('params', {}),
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floats=['gamma'],
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strs=['affinity'],
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ints=['k', 'random_state'],
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aliases={'k': 'n_clusters'},
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)
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self.estimator = _SpectralClustering(**out_params)
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self.scaler = StandardScaler()
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def fit(self, df, options):
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# Make a copy of data, to not alter original dataframe
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X = df.copy()
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X, nans, _ = df_util.prepare_features(
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X=X, variables=self.feature_variables, mlspl_limits=options.get('mlspl_limits')
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)
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if nans.any():
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# If null values found in the data, warn the user to handle them before fit.
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cexc.messages.warn(
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"NULL values found in the dataset. Clusters are not assigned for these values currently. "
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"Please consider handling null (or missing) entries to get appropriate clustering output."
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)
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if len(X) > 0 and len(X) <= self.estimator.n_clusters:
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raise RuntimeError("k must be smaller than the number of events used as input")
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scaled_X = self.scaler.fit_transform(X.values)
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y_hat = self.estimator.fit_predict(scaled_X)
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y_hat = ['' if np.isnan(v) else str('%.0f' % v) for v in y_hat]
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default_name = 'cluster'
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output_name = options.get('output_name', default_name)
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output = df_util.create_output_dataframe(
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y_hat=y_hat, nans=nans, output_names=output_name
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)
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df = df_util.merge_predictions(df, output)
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return df
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